llmcompressor.datasets.utils

def get_calibration_dataloader(
    dataset_args: DatasetArguments,
    processor: Processor,
) -> torch.utils.data.DataLoader:
    """
    Get the dataloader used for oneshot calibration.
    :param dataset_args: DatasetArguments that contains the dataset parameters.
    :param processor: Processor or the tokenizer of the model.
    :return: PyTorch dataloader object that contains the calibration dataset.
    """
    if dataset_args.dataset is None:
        # weight-only quantization or dynamic quantization
        return

    datasets = get_processed_dataset(
        dataset_args=dataset_args,
        processor=processor,
        do_oneshot=True,
        do_train=False,
    )
    calibration_dataset = datasets.get("calibration")

    return format_calibration_data(dataset_args, calibration_dataset, processor)

def get_processed_dataset(
    dataset_args: DatasetArguments,
    processor: Processor | None = None,
    do_oneshot: bool = False,
    do_train: bool = True,
) -> dict[str, Dataset] | None:
    """
    Loads datasets for each flow based on dataset_args, stores a Dataset for each
    enabled flow in datasets
    :param dataset_args: DatasetArguments that contain dataset loading and
        processing params
    :param processor: processor or tokenizer to use for dataset tokenization
    :param do_oneshot: True for oneshot pathway
    :param do_train: True for train pathway
    :return: A dataset corresponding to either train or calibration (oneshot)
    """
    if dataset_args.dataset is None:
        logger.warning(
            "Running oneshot without calibration data. This is expected for "
            "weight-only and dynamic quantization"
        )
        return

    splits = dataset_args.splits
    tokenized_datasets = {}

    def _get_split_name(inp_str):
        # strip out split name, for ex train[60%:] -> train
        split_name_match = re.match(r"(\w*)\[.*\]", inp_str)
        if split_name_match is not None:
            return split_name_match.group(1)
        return inp_str

    match splits:
        case None:
            splits = {"all": None}
        case str():
            splits = {_get_split_name(splits): splits}
        case list():
            splits = {_get_split_name(s): s for s in splits}
        case dict():
            pass
        case _:
            raise ValueError(f"Invalid splits type: {type(splits)}")

    # default to custom dataset if dataset provided isn't a string
    registry_id = (
        dataset_args.dataset if isinstance(dataset_args.dataset, str) else "custom"
    )
    for split_name, split_str in splits.items():
        dataset = dataset_args.dataset
        if hasattr(dataset, "column_names") and "input_ids" in dataset.column_names:
            # dataset is already tokenized
            tokenized_datasets[split_name] = dataset
        else:
            # dataset needs to be tokenized
            dataset_manager = TextGenerationDataset.load_from_registry(
                registry_id,
                dataset_args=dataset_args,
                split=split_str,
                processor=processor,
            )
            tokenized_datasets[split_name] = dataset_manager(add_labels=do_train)

    return make_dataset_splits(
        tokenized_datasets,
        do_oneshot=do_oneshot,
        do_train=do_train,
    )

def get_rank_partition(split: str, num_samples: int) -> str:
    """
    Utility for splitting data in a distributed setting

    :param split: the split string to partition, e.g. "train"
    :param num_samples: the total number of samples in the dataset to partition
    :return: a partitioned split string

    Usage example:

    DATASET_ID = "HuggingFaceH4/ultrachat_200k"
    DATASET_SPLIT = "train_sft"
    NUM_CALIBRATION_SAMPLES = 256

    split = get_rank_partition(DATASET_SPLIT, NUM_CALIBRATION_SAMPLES)
    ds = load_dataset(
        DATASET_ID,
        split=split,
    )

    for S samples and D devices, when S is not perfectly divisible by D,
    we give each device at least S//D samples and distribute
    the remaining samples as evenly as possible across all devices
    """
    assert (
        "[" not in split
    ), "Split string should not already contain partitioning brackets"

    start, end = _get_partition_start_end(
        num_samples, dist.get_rank(), dist.get_world_size()
    )
    return f"{split}[{start}:{end}]"

def make_dataset_splits(
    tokenized_datasets: dict[str, Any],
    do_oneshot: bool = True,
    do_train: bool = False,
) -> dict[str, Dataset]:
    """
    Restructures the datasets dictionary based on what tasks will be run
    train
    :param tokenized_datasets: dictionary of processed datasets
    :param do_oneshot: Whether to store the calibration dataset
    :return: A dataset corresponding to either train or calibration (oneshot)
    """

    # handles case where all splits are contained in a single dataset
    if "all" in tokenized_datasets and len(tokenized_datasets) == 1:
        tokenized_datasets = tokenized_datasets.get("all")
        if isinstance(tokenized_datasets, Dataset):
            tokenized_datasets = {"train": tokenized_datasets}

    train_split = calib_split = None

    if do_train:
        if "train" not in tokenized_datasets:
            raise ValueError("--do_train requires a train dataset")
        train_split = tokenized_datasets["train"]
    if do_oneshot:
        calib_split = tokenized_datasets.get("calibration")
        if calib_split is None:
            if "train" not in tokenized_datasets:
                raise ValueError("--do_oneshot requires a calibration dataset")
            calib_split = tokenized_datasets["train"]

    split_datasets = {
        "train": train_split,
        "calibration": calib_split,
    }
    return split_datasets